ATHENS — Conno Christou, 35, had just received the best bloodwork of his life. He tracked sleep with a Whoop band, cross-referenced it with an Oura ring, and followed protocols from longevity researchers Peter Attia and Rhonda Patrick. Then, after a workout, his arm swelled. A week later, doctors found two blood clots. The pre-op scan for surgery revealed something else entirely: an 11-by-11-by-8 centimeter mass behind his sternum.
Biopsy confirmed an aggressive, fast-growing form of non-Hodgkin’s lymphoma — a rare diagnosis affecting roughly one in 420,000 people. The tumor had existed for about three months. In three more weeks, it would have reached stage four. “Lucky in my unluckiness,” Christou told this editor from his home in Athens. “It was only found because I went in for something else entirely.”
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Two world-class doctors, opposite recommendations
His first oncologist recommended the lighter of two available chemotherapy regimens. Christou booked his first infusion three days out. The night before, he sought a second opinion. That doctor recommended the harder regimen — continuous in-hospital infusion, cycling every three weeks across six months — citing Christou’s specific pathology. The lighter treatment carried roughly a 60% success rate for his presentation. The aggressive one brought that number to around 85%.
“As founders, we hold the wheel,” Christou says. “You hear many things. You don’t have to follow the first advice.” Over the next two days, he gathered 12 opinions total — drawing on his professional network, reaching out to hematologists and oncologists in the US and abroad. Eleven to one voted in favor of the harder path. He took it.
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Chemotherapy as a data-driven marathon
Over six months of treatment, Christou approached chemotherapy like building a company: a marathon of sprints, each with a finite cycle, each week filled with data points. He wore his Whoop throughout and found it remarkably accurate at predicting the days his immune system would bottom out. He kept a symptom journal using voice transcription, logging every shift, every side effect, every medication.
He narrowed his focus to three variables: sleep, nutrition, and psychology. “It moves the needle more than anything,” he said. “I never asked ‘why me’ — not once. That question has no useful answer.” He fed all of it — blood results, scan data, wearable output, journal entries — into Claude.
A public opinion poll released in March found that a third of American adults now use chatbots for health information and advice. Experts urge caution; Danielle Bitterman, clinical lead for data science and AI at Mass General Brigham, has told the New York Times that general-purpose chatbots are frequently wrong and “have not been thoroughly evaluated” for personalized diagnoses. Christou doesn’t disagree. “It didn’t replace the doctors,” he says, but it “helped me ask the right questions.”
The PET scan that almost changed everything
At the end of treatment, his final PET scan came back ambiguous. His oncologist began discussing a second line of therapy, potentially radiotherapy near his heart and lungs. Christou again did his homework. He read that for this specific lymphoma, the false-positive rate on end-of-treatment PET scans is around 60% — a statistic that still astonishes him. “It’s 2026,” he says. “Sixty percent.”
He fed all three of his PET scans and his MRI into Claude, which flagged a known but easily overlooked phenomenon: in patients under 40 recovering from this type of lymphoma, the thymus gland can reactivate after chemotherapy, showing up on imaging as what appears to be active disease. Given his age and scan characteristics, the model put the probability of that explanation at roughly 90%. He sought three more opinions. The fourth doctor confirmed it: thymus rebound. There was no active disease. No radiotherapy was needed.
A founder’s perspective on the system
Christou built Keragon, his current company, before any of this happened; it’s an AI-powered platform that helps medical practices automate administrative operations. Going through the system as a patient gave him new perspective. He watched nurses and doctors buried under tasks that had nothing to do with care. He received the same chemotherapy protocol as an 80-year-old woman, the side effects managed through a cascading chain of additional drugs. He says he’s certain that we will look back at this era of treatment and cringe.
He takes Sundays off now, mostly. He tries to be present — at lunch with friends, at home with his dog, in conversations that might once have felt like a distraction from work. A VC friend told him something years ago that he kept replaying during treatment: Be happy now. He says it’s among the hardest things to do and yet he finally appreciates its importance. “It’s not happening in 10 years,” he says of what AI can already do for patients willing to use it. “It’s happening today.”
Frequently Asked Questions
What kind of cancer did Conno Christou have?
He was diagnosed with an aggressive, fast-growing form of non-Hodgkin’s lymphoma, a rare condition affecting roughly one in 420,000 people.
How did AI help him during treatment?
He fed blood results, scan data, wearable output, and journal entries into Claude. The AI helped him identify a thymus rebound phenomenon on his PET scan, which avoided unnecessary radiotherapy.
Did AI replace his doctors?
No. Christou gathered 12 medical opinions and used AI to help formulate better questions for his physicians. He emphasizes that AI supplemented, not replaced, professional medical advice.
What is Keragon?
Keragon is Christou’s AI-powered platform that helps medical practices automate administrative operations. He built it before his diagnosis.
What is the false-positive rate for end-of-treatment PET scans for this lymphoma?
Christou says the false-positive rate is around 60%, a statistic he found astonishing. AI helped him contextualize that risk.
